Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations2372474
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 GiB
Average record size in memory565.3 B

Variable types

Text4
Numeric7
DateTime4
Categorical1

Alerts

Brand is highly overall correlated with ClassificationHigh correlation
Classification is highly overall correlated with BrandHigh correlation
Dollars is highly overall correlated with PurchasePrice and 1 other fieldsHigh correlation
PurchasePrice is highly overall correlated with DollarsHigh correlation
Quantity is highly overall correlated with DollarsHigh correlation
PurchasePrice is highly skewed (γ1 = 106.3858573) Skewed
Dollars is highly skewed (γ1 = 30.42890051) Skewed

Reproduction

Analysis started2025-09-25 08:07:09.653548
Analysis finished2025-09-25 08:09:42.708747
Duration2 minutes and 33.06 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct245907
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size149.4 MiB
2025-09-25T14:09:43.724607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length21
Mean length17.045803
Min length6

Characters and Unicode

Total characters40440724
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56671 ?
Unique (%)2.4%

Sample

1st row69_MOUNTMEND_8412
2nd row30_CULCHETH_5255
3rd row34_PITMERDEN_5215
4th row1_HARDERSFIELD_5255
5th row76_DONCASTER_2034
ValueCountFrequency (%)
55_dry 46820
 
1.9%
56_beggar's 30429
 
1.2%
42_black 18198
 
0.7%
47_pella's 14976
 
0.6%
26_knife's 3974
 
0.2%
73_doncaster_8068 180
 
< 0.1%
73_doncaster_3545 178
 
< 0.1%
76_doncaster_1233 175
 
< 0.1%
76_doncaster_5364 174
 
< 0.1%
67_eanverness_3545 171
 
< 0.1%
Other values (245902) 2371596
95.4%
2025-09-25T14:09:45.219594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4744948
 
11.7%
E 2562543
 
6.3%
R 2140431
 
5.3%
3 2050600
 
5.1%
N 1902767
 
4.7%
A 1771034
 
4.4%
2 1702043
 
4.2%
6 1688830
 
4.2%
1 1665032
 
4.1%
4 1625850
 
4.0%
Other values (25) 18586646
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40440724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 4744948
 
11.7%
E 2562543
 
6.3%
R 2140431
 
5.3%
3 2050600
 
5.1%
N 1902767
 
4.7%
A 1771034
 
4.4%
2 1702043
 
4.2%
6 1688830
 
4.2%
1 1665032
 
4.1%
4 1625850
 
4.0%
Other values (25) 18586646
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40440724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 4744948
 
11.7%
E 2562543
 
6.3%
R 2140431
 
5.3%
3 2050600
 
5.1%
N 1902767
 
4.7%
A 1771034
 
4.4%
2 1702043
 
4.2%
6 1688830
 
4.2%
1 1665032
 
4.1%
4 1625850
 
4.0%
Other values (25) 18586646
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40440724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 4744948
 
11.7%
E 2562543
 
6.3%
R 2140431
 
5.3%
3 2050600
 
5.1%
N 1902767
 
4.7%
A 1771034
 
4.4%
2 1702043
 
4.2%
6 1688830
 
4.2%
1 1665032
 
4.1%
4 1625850
 
4.0%
Other values (25) 18586646
46.0%

Store
Real number (ℝ)

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.651328
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:45.419237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q125
median48
Q367
95-th percentile76
Maximum81
Range80
Interquartile range (IQR)42

Descriptive statistics

Standard deviation23.512448
Coefficient of variation (CV)0.52657892
Kurtosis-1.2139221
Mean44.651328
Median Absolute Deviation (MAD)20
Skewness-0.24595524
Sum1.0593411 × 108
Variance552.83521
MonotonicityNot monotonic
2025-09-25T14:09:45.654340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 121123
 
5.1%
73 96794
 
4.1%
38 93412
 
3.9%
34 92113
 
3.9%
66 82699
 
3.5%
67 69419
 
2.9%
50 65512
 
2.8%
69 63683
 
2.7%
60 58169
 
2.5%
41 53344
 
2.2%
Other values (70) 1576206
66.4%
ValueCountFrequency (%)
1 42370
1.8%
2 28056
1.2%
3 1806
 
0.1%
4 14483
 
0.6%
5 10441
 
0.4%
6 39393
1.7%
7 31044
1.3%
8 23414
1.0%
9 33298
1.4%
10 41413
1.7%
ValueCountFrequency (%)
81 9407
 
0.4%
79 28559
 
1.2%
78 17544
 
0.7%
77 22823
 
1.0%
76 121123
5.1%
75 22356
 
0.9%
74 40723
 
1.7%
73 96794
4.1%
72 32784
 
1.4%
71 25727
 
1.1%

Brand
Real number (ℝ)

High correlation 

Distinct10664
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12418.641
Minimum58
Maximum90631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:45.914640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile1376
Q13639
median6523
Q318877
95-th percentile40097
Maximum90631
Range90573
Interquartile range (IQR)15238

Descriptive statistics

Standard deviation12557.278
Coefficient of variation (CV)1.0111636
Kurtosis0.39302954
Mean12418.641
Median Absolute Deviation (MAD)3529
Skewness1.2686906
Sum2.9462903 × 1010
Variance1.5768524 × 108
MonotonicityNot monotonic
2025-09-25T14:09:46.132914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8068 7359
 
0.3%
3893 7166
 
0.3%
4261 6774
 
0.3%
1233 6584
 
0.3%
3405 6506
 
0.3%
3512 6459
 
0.3%
3243 6419
 
0.3%
3246 6386
 
0.3%
1376 6336
 
0.3%
3545 6326
 
0.3%
Other values (10654) 2306159
97.2%
ValueCountFrequency (%)
58 596
< 0.1%
60 97
 
< 0.1%
61 26
 
< 0.1%
62 567
< 0.1%
63 524
< 0.1%
70 3
 
< 0.1%
72 77
 
< 0.1%
75 1
 
< 0.1%
77 842
< 0.1%
79 496
< 0.1%
ValueCountFrequency (%)
90631 131
< 0.1%
90609 11
 
< 0.1%
90604 13
 
< 0.1%
90090 2
 
< 0.1%
90089 9
 
< 0.1%
90088 2
 
< 0.1%
90087 5
 
< 0.1%
90086 1
 
< 0.1%
90085 3
 
< 0.1%
90080 1
 
< 0.1%
Distinct9652
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size158.2 MiB
2025-09-25T14:09:46.720006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length23
Mean length20.935902
Min length6

Characters and Unicode

Total characters49669884
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1271 ?
Unique (%)0.1%

Sample

1st rowTequila Ocho Plata Fresno
2nd rowTGI Fridays Ultimte Mudslide
3rd rowTGI Fridays Long Island Iced
4th rowTGI Fridays Ultimte Mudslide
5th rowGlendalough Double Barrel
ValueCountFrequency (%)
vodka 267350
 
3.2%
svgn 187706
 
2.2%
chard 168648
 
2.0%
pnt 136783
 
1.6%
cab 134965
 
1.6%
rum 131678
 
1.6%
cal 109124
 
1.3%
grigio 81848
 
1.0%
red 76942
 
0.9%
black 76733
 
0.9%
Other values (8173) 7027497
83.7%
2025-09-25T14:09:47.467415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6026961
 
12.1%
a 4116492
 
8.3%
e 3571756
 
7.2%
o 3118929
 
6.3%
r 3055020
 
6.2%
n 2627269
 
5.3%
i 2501223
 
5.0%
l 2379802
 
4.8%
t 1627528
 
3.3%
s 1486233
 
3.0%
Other values (65) 19158671
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49669884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6026961
 
12.1%
a 4116492
 
8.3%
e 3571756
 
7.2%
o 3118929
 
6.3%
r 3055020
 
6.2%
n 2627269
 
5.3%
i 2501223
 
5.0%
l 2379802
 
4.8%
t 1627528
 
3.3%
s 1486233
 
3.0%
Other values (65) 19158671
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49669884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6026961
 
12.1%
a 4116492
 
8.3%
e 3571756
 
7.2%
o 3118929
 
6.3%
r 3055020
 
6.2%
n 2627269
 
5.3%
i 2501223
 
5.0%
l 2379802
 
4.8%
t 1627528
 
3.3%
s 1486233
 
3.0%
Other values (65) 19158671
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49669884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6026961
 
12.1%
a 4116492
 
8.3%
e 3571756
 
7.2%
o 3118929
 
6.3%
r 3055020
 
6.2%
n 2627269
 
5.3%
i 2501223
 
5.0%
l 2379802
 
4.8%
t 1627528
 
3.3%
s 1486233
 
3.0%
Other values (65) 19158671
38.6%

Size
Text

Distinct51
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size121.7 MiB
2025-09-25T14:09:47.635038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length5
Mean length4.7696086
Min length2

Characters and Unicode

Total characters11315758
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row750mL
2nd row1.75L
3rd row1.75L
4th row1.75L
5th row750mL
ValueCountFrequency (%)
750ml 1208612
50.1%
1.75l 593298
24.6%
1.5l 229841
 
9.5%
50ml 74965
 
3.1%
375ml 70635
 
2.9%
3l 55911
 
2.3%
5l 53490
 
2.2%
liter 47520
 
2.0%
pk 20175
 
0.8%
4 17504
 
0.7%
Other values (31) 42774
 
1.8%
2025-09-25T14:09:47.998748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2372127
21.0%
5 2239758
19.8%
7 1889830
16.7%
m 1385690
12.2%
0 1310135
11.6%
1 843312
 
7.5%
. 823476
 
7.3%
3 129884
 
1.1%
i 47520
 
0.4%
t 47520
 
0.4%
Other values (18) 226506
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11315758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 2372127
21.0%
5 2239758
19.8%
7 1889830
16.7%
m 1385690
12.2%
0 1310135
11.6%
1 843312
 
7.5%
. 823476
 
7.3%
3 129884
 
1.1%
i 47520
 
0.4%
t 47520
 
0.4%
Other values (18) 226506
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11315758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 2372127
21.0%
5 2239758
19.8%
7 1889830
16.7%
m 1385690
12.2%
0 1310135
11.6%
1 843312
 
7.5%
. 823476
 
7.3%
3 129884
 
1.1%
i 47520
 
0.4%
t 47520
 
0.4%
Other values (18) 226506
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11315758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 2372127
21.0%
5 2239758
19.8%
7 1889830
16.7%
m 1385690
12.2%
0 1310135
11.6%
1 843312
 
7.5%
. 823476
 
7.3%
3 129884
 
1.1%
i 47520
 
0.4%
t 47520
 
0.4%
Other values (18) 226506
 
2.0%

VendorNumber
Real number (ℝ)

Distinct126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6886.4355
Minimum2
Maximum201359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:48.147831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile653
Q13252
median4425
Q39552
95-th percentile17035
Maximum201359
Range201357
Interquartile range (IQR)6300

Descriptive statistics

Standard deviation8066.6939
Coefficient of variation (CV)1.1713889
Kurtosis90.903897
Mean6886.4355
Median Absolute Deviation (MAD)3297
Skewness7.9128421
Sum1.6337889 × 1010
Variance65071550
MonotonicityNot monotonic
2025-09-25T14:09:48.368508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3960 243326
 
10.3%
12546 189832
 
8.0%
1392 185574
 
7.8%
4425 176781
 
7.5%
3252 162567
 
6.9%
9552 112792
 
4.8%
17035 107612
 
4.5%
9815 106106
 
4.5%
8004 92210
 
3.9%
480 91846
 
3.9%
Other values (116) 903828
38.1%
ValueCountFrequency (%)
2 13
 
< 0.1%
54 1
 
< 0.1%
60 626
 
< 0.1%
105 57
 
< 0.1%
200 8
 
< 0.1%
287 18
 
< 0.1%
388 266
 
< 0.1%
480 91846
3.9%
516 23510
 
1.0%
653 11406
 
0.5%
ValueCountFrequency (%)
201359 1
 
< 0.1%
173357 153
 
< 0.1%
172662 149
 
< 0.1%
99166 41
 
< 0.1%
98450 1509
0.1%
90059 44
 
< 0.1%
90058 886
< 0.1%
90057 74
 
< 0.1%
90056 1393
0.1%
90053 231
 
< 0.1%
Distinct129
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size170.9 MiB
2025-09-25T14:09:48.852095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length27
Mean length26.524408
Min length10

Characters and Unicode

Total characters62928468
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowALTAMAR BRANDS LLC
2nd rowAMERICAN VINTAGE BEVERAGE
3rd rowAMERICAN VINTAGE BEVERAGE
4th rowAMERICAN VINTAGE BEVERAGE
5th rowATLANTIC IMPORTING COMPANY
ValueCountFrequency (%)
inc 1119633
 
13.8%
brands 399251
 
4.9%
america 327160
 
4.0%
north 297657
 
3.7%
company 278913
 
3.4%
diageo 255682
 
3.2%
usa 248319
 
3.1%
242464
 
3.0%
wine 216171
 
2.7%
beam 189832
 
2.3%
Other values (226) 4516330
55.8%
2025-09-25T14:09:49.416987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20632582
32.8%
A 4552799
 
7.2%
I 4312189
 
6.9%
N 4151688
 
6.6%
E 3673966
 
5.8%
R 3325475
 
5.3%
C 3033633
 
4.8%
O 2635301
 
4.2%
S 2322172
 
3.7%
T 2291543
 
3.6%
Other values (36) 11997120
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62928468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20632582
32.8%
A 4552799
 
7.2%
I 4312189
 
6.9%
N 4151688
 
6.6%
E 3673966
 
5.8%
R 3325475
 
5.3%
C 3033633
 
4.8%
O 2635301
 
4.2%
S 2322172
 
3.7%
T 2291543
 
3.6%
Other values (36) 11997120
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62928468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20632582
32.8%
A 4552799
 
7.2%
I 4312189
 
6.9%
N 4151688
 
6.6%
E 3673966
 
5.8%
R 3325475
 
5.3%
C 3033633
 
4.8%
O 2635301
 
4.2%
S 2322172
 
3.7%
T 2291543
 
3.6%
Other values (36) 11997120
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62928468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20632582
32.8%
A 4552799
 
7.2%
I 4312189
 
6.9%
N 4151688
 
6.6%
E 3673966
 
5.8%
R 3325475
 
5.3%
C 3033633
 
4.8%
O 2635301
 
4.2%
S 2322172
 
3.7%
T 2291543
 
3.6%
Other values (36) 11997120
19.1%

PONumber
Real number (ℝ)

Distinct5543
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11040.937
Minimum8106
Maximum13661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:49.581831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8106
5-th percentile8503
Q19761
median11103
Q312397
95-th percentile13436
Maximum13661
Range5555
Interquartile range (IQR)2636

Descriptive statistics

Standard deviation1565.3402
Coefficient of variation (CV)0.14177603
Kurtosis-1.1281758
Mean11040.937
Median Absolute Deviation (MAD)1322
Skewness-0.076414296
Sum2.6194335 × 1010
Variance2450290
MonotonicityNot monotonic
2025-09-25T14:09:49.814116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10936 6108
 
0.3%
13487 5594
 
0.2%
11300 5532
 
0.2%
13597 5532
 
0.2%
11191 5529
 
0.2%
13392 5488
 
0.2%
13226 5481
 
0.2%
12149 5382
 
0.2%
11028 5286
 
0.2%
10260 5168
 
0.2%
Other values (5533) 2317374
97.7%
ValueCountFrequency (%)
8106 368
< 0.1%
8107 2
 
< 0.1%
8108 585
< 0.1%
8109 67
 
< 0.1%
8110 571
< 0.1%
8111 2
 
< 0.1%
8112 38
 
< 0.1%
8113 91
 
< 0.1%
8114 72
 
< 0.1%
8115 111
 
< 0.1%
ValueCountFrequency (%)
13661 2099
0.1%
13660 18
 
< 0.1%
13659 34
 
< 0.1%
13658 734
 
< 0.1%
13657 31
 
< 0.1%
13656 25
 
< 0.1%
13655 27
 
< 0.1%
13654 903
 
< 0.1%
13653 431
 
< 0.1%
13652 5088
0.2%

PODate
Date

Distinct319
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
Minimum2023-12-20 00:00:00
Maximum2024-12-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-25T14:09:50.046977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:50.299811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct364
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-25T14:09:50.547492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:50.815728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct373
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
Minimum2024-01-04 00:00:00
Maximum2025-01-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-25T14:09:51.079897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:51.365280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct382
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
Minimum2024-02-04 00:00:00
Maximum2025-02-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-25T14:09:51.732187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:51.999059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PurchasePrice
Real number (ℝ)

High correlation  Skewed 

Distinct2151
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.05005
Minimum0
Maximum5681.81
Zeros153
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:52.261602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.37
Q16.12
median9.22
Q314.49
95-th percentile28.88
Maximum5681.81
Range5681.81
Interquartile range (IQR)8.37

Descriptive statistics

Standard deviation17.945104
Coefficient of variation (CV)1.4892141
Kurtosis22388.512
Mean12.05005
Median Absolute Deviation (MAD)3.51
Skewness106.38586
Sum28588431
Variance322.02677
MonotonicityNot monotonic
2025-09-25T14:09:52.480174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.84 21743
 
0.9%
7.93 18223
 
0.8%
7.68 13385
 
0.6%
9.62 13252
 
0.6%
9.08 13191
 
0.6%
10.38 13186
 
0.6%
6.53 13163
 
0.6%
6.75 12942
 
0.5%
8.44 12857
 
0.5%
4.79 12306
 
0.5%
Other values (2141) 2228226
93.9%
ValueCountFrequency (%)
0 153
 
< 0.1%
0.36 533
 
< 0.1%
0.38 949
 
< 0.1%
0.39 1
 
< 0.1%
0.62 395
 
< 0.1%
0.64 61
 
< 0.1%
0.71 2909
 
0.1%
0.72 10602
0.4%
0.73 999
 
< 0.1%
0.74 7543
0.3%
ValueCountFrequency (%)
5681.81 3
 
< 0.1%
4264.7 2
 
< 0.1%
3352.93 3
 
< 0.1%
2713.17 1
 
< 0.1%
2661.86 7
 
< 0.1%
2518.51 1
 
< 0.1%
2290.07 27
< 0.1%
2222.21 3
 
< 0.1%
2109.37 2
 
< 0.1%
1574.8 7
 
< 0.1%

Quantity
Real number (ℝ)

High correlation 

Distinct686
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.155846
Minimum1
Maximum3816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:52.946478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median10
Q312
95-th percentile46
Maximum3816
Range3815
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.446162
Coefficient of variation (CV)1.6562882
Kurtosis759.02166
Mean14.155846
Median Absolute Deviation (MAD)4
Skewness13.73724
Sum33584377
Variance549.72249
MonotonicityNot monotonic
2025-09-25T14:09:53.150620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 627805
26.5%
6 499448
21.1%
11 171280
 
7.2%
5 160343
 
6.8%
4 129921
 
5.5%
10 99799
 
4.2%
3 90988
 
3.8%
24 57464
 
2.4%
9 55971
 
2.4%
2 44908
 
1.9%
Other values (676) 434547
18.3%
ValueCountFrequency (%)
1 43481
 
1.8%
2 44908
 
1.9%
3 90988
 
3.8%
4 129921
 
5.5%
5 160343
 
6.8%
6 499448
21.1%
7 26913
 
1.1%
8 42514
 
1.8%
9 55971
 
2.4%
10 99799
 
4.2%
ValueCountFrequency (%)
3816 1
< 0.1%
2425 1
< 0.1%
2159 1
< 0.1%
2055 1
< 0.1%
1920 1
< 0.1%
1910 1
< 0.1%
1896 1
< 0.1%
1800 2
< 0.1%
1793 1
< 0.1%
1763 1
< 0.1%

Dollars
Real number (ℝ)

High correlation  Skewed 

Distinct33567
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.68147
Minimum0
Maximum50175.7
Zeros153
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.1 MiB
2025-09-25T14:09:53.381350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.8
Q149.26
median83.93
Q3140.52
95-th percentile377.76
Maximum50175.7
Range50175.7
Interquartile range (IQR)91.26

Descriptive statistics

Standard deviation281.66494
Coefficient of variation (CV)2.0759278
Kurtosis2321.0212
Mean135.68147
Median Absolute Deviation (MAD)40.85
Skewness30.428901
Sum3.2190077 × 108
Variance79335.139
MonotonicityNot monotonic
2025-09-25T14:09:53.595227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.96 7595
 
0.3%
63.12 7404
 
0.3%
85.68 6978
 
0.3%
78.36 6320
 
0.3%
72.84 5885
 
0.2%
73.44 5573
 
0.2%
71.4 5408
 
0.2%
78.84 5323
 
0.2%
55.2 5102
 
0.2%
134.28 5088
 
0.2%
Other values (33557) 2311798
97.4%
ValueCountFrequency (%)
0 153
< 0.1%
0.36 7
 
< 0.1%
0.38 11
 
< 0.1%
0.62 13
 
< 0.1%
0.64 6
 
< 0.1%
0.71 112
< 0.1%
0.72 74
< 0.1%
0.73 35
 
< 0.1%
0.74 92
< 0.1%
0.75 40
 
< 0.1%
ValueCountFrequency (%)
50175.7 1
< 0.1%
39503.25 1
< 0.1%
38949.9 1
< 0.1%
36517.35 1
< 0.1%
35506.82 1
< 0.1%
32465.68 1
< 0.1%
32445.54 1
< 0.1%
31473.31 1
< 0.1%
30578.28 1
< 0.1%
30534.79 1
< 0.1%

Classification
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.1 MiB
1
1320234 
2
1052240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2372474
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Length

2025-09-25T14:09:53.811734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T14:09:53.944896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Most occurring characters

ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2372474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2372474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2372474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1320234
55.6%
2 1052240
44.4%

Interactions

2025-09-25T14:09:17.899102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:34.020853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:39.451057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:45.197953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:51.278352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:57.907062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:09.552055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:18.801064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:34.820443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:40.234109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:46.046817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:52.161663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:59.136182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:10.568181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:19.715019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:35.569987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:41.033597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:46.847370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:53.044612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:01.347049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:11.886535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:20.662667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:36.336141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:41.868617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:47.746618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:53.960769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:03.046169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:13.238684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:21.545644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:37.135781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:42.653510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:48.615600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:54.883075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:04.925701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:14.315482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:22.446971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:37.901902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:43.665482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:49.495965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:55.776310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:06.423383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:15.404100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:23.348569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:38.685117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:44.415302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:50.394399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:08:56.825388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:08.092482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T14:09:16.922979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-25T14:09:54.045036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BrandClassificationDollarsPONumberPurchasePriceQuantityStoreVendorNumber
Brand1.0000.819-0.2580.010-0.4330.1040.037-0.031
Classification0.8191.0000.0130.0170.0050.0080.0630.071
Dollars-0.2580.0131.0000.0360.5590.5880.0380.057
PONumber0.0100.0170.0361.0000.0190.0200.018-0.007
PurchasePrice-0.4330.0050.5590.0191.000-0.2570.0380.069
Quantity0.1040.0080.5880.020-0.2571.0000.0060.001
Store0.0370.0630.0380.0180.0380.0061.0000.002
VendorNumber-0.0310.0710.057-0.0070.0690.0010.0021.000

Missing values

2025-09-25T14:09:24.660582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-25T14:09:29.611958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

InventoryIdStoreBrandDescriptionSizeVendorNumberVendorNamePONumberPODateReceivingDateInvoiceDatePayDatePurchasePriceQuantityDollarsClassification
069_MOUNTMEND_8412698412Tequila Ocho Plata Fresno750mL105ALTAMAR BRANDS LLC81242023-12-212024-01-022024-01-042024-02-1635.716214.261
130_CULCHETH_5255305255TGI Fridays Ultimte Mudslide1.75L4466AMERICAN VINTAGE BEVERAGE81372023-12-222024-01-012024-01-072024-02-219.35437.401
234_PITMERDEN_5215345215TGI Fridays Long Island Iced1.75L4466AMERICAN VINTAGE BEVERAGE81372023-12-222024-01-022024-01-072024-02-219.41547.051
31_HARDERSFIELD_525515255TGI Fridays Ultimte Mudslide1.75L4466AMERICAN VINTAGE BEVERAGE81372023-12-222024-01-012024-01-072024-02-219.35656.101
476_DONCASTER_2034762034Glendalough Double Barrel750mL388ATLANTIC IMPORTING COMPANY81692023-12-242024-01-022024-01-092024-02-1621.325106.601
55_SUTTON_334853348Bombay Sapphire Gin1.75L480BACARDI USA INC81062023-12-202024-01-022024-01-122024-02-0522.386134.281
61_HARDERSFIELD_835818358Bacardi 151 Proof750mL480BACARDI USA INC81062023-12-202024-01-012024-01-122024-02-0514.4912173.881
730_CULCHETH_4903304903Bacardi Superior Rum200mL480BACARDI USA INC81062023-12-202024-01-012024-01-122024-02-052.8748137.761
834_PITMERDEN_3782343782Grey Goose Le Citron Vodka750mL480BACARDI USA INC81062023-12-202024-01-022024-01-122024-02-0518.89594.451
91_HARDERSFIELD_423314233Castillo Silver Label Rum1.75L480BACARDI USA INC81062023-12-202024-01-012024-01-122024-02-057.8723181.011
InventoryIdStoreBrandDescriptionSizeVendorNumberVendorNamePONumberPODateReceivingDateInvoiceDatePayDatePurchasePriceQuantityDollarsClassification
237246479_BALLYMENA_195567919556Zorvino Bacca Z Blackberry750mL90058ZORVINO VINEYARDS135932024-12-192024-12-312025-01-092025-02-069.3912112.682
237246569_MOUNTMEND_251256925125Zorvino Vyds Mango Magnifico750mL90058ZORVINO VINEYARDS135932024-12-192024-12-302025-01-092025-02-068.551194.052
237246669_MOUNTMEND_195566919556Zorvino Bacca Z Blackberry750mL90058ZORVINO VINEYARDS135932024-12-192024-12-302025-01-092025-02-069.3912112.682
237246779_BALLYMENA_195577919557Zorvino Fragole Z Strawberry750mL90058ZORVINO VINEYARDS135932024-12-192024-12-312025-01-092025-02-069.3911103.292
237246867_EANVERNESS_251266725126Zorvino Vyds Peachez750mL90058ZORVINO VINEYARDS135932024-12-192024-12-282025-01-092025-02-066.621172.822
237246949_GARIGILL_222984922298Zorvino Vyds Sangiovese750mL90058ZORVINO VINEYARDS135932024-12-192024-12-282025-01-092025-02-068.061296.722
23724701_HARDERSFIELD_19556119556Zorvino Bacca Z Blackberry750mL90058ZORVINO VINEYARDS135932024-12-192024-12-272025-01-092025-02-069.3912112.682
237247166_EANVERNESS_222976622297Zorvino Vyds Pearz750mL90058ZORVINO VINEYARDS135932024-12-192024-12-262025-01-092025-02-066.751281.002
237247269_MOUNTMEND_195576919557Zorvino Fragole Z Strawberry750mL90058ZORVINO VINEYARDS135932024-12-192024-12-262025-01-092025-02-069.3912112.682
237247355_DRY GULCH_222985522298Zorvino Vyds Sangiovese750mL90058ZORVINO VINEYARDS135932024-12-192024-12-282025-01-092025-02-068.061296.722